
where \(p\in \mathcal {V}_P\)  is a property, \(s\in \mathcal {N}(p)\)  is a sentence having property \(p\) , \(\mathrm {MEAN}(\cdot )\)  is the mean aggregator [1]}, and \(\mathrm {FFN}(\cdot )\)  is the feed-forward network.
\(\mathrm {FFN}(\cdot )\)  can be a linear layer in SGC [2]}, a linear layer followed by a nonlinear activation function in GraphSAGE [1]}, or a multi-layer perception in GIN [4]}, etc.
We follow SGC [2]} to implement \(\mathrm {FFN}(\cdot )\)  by default.
For each property \(p\) , this layer aggregates the sentence-level representations \(\mathbf {h}_s(t)\)  from \(s \in \mathcal {N}(p)\)  to obtain a global property embedding \(\mathbf {h}_p(t)\) .
In this way, the generalized context of each property is captured from the whole dataset, which is further used to enhance the relation representations for each sentence in the second GNN layer.
We describe the details of the second GNN layer in sec:second-GNN.
